Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors.

ABSTRACT: Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we pre

SUBMITTER: Du M 

PROVIDER: S-EPMC11301988 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature